Evidence map›Paper›PMID 38253908›Full record

ArticleEuropean journal of nuclear medicine and molecular imaging2024

Downward myocardial creep during stress PET imaging is inversely associated with mortality.

Keiichiro Kuronuma, Robert J H Miller, Chih-Chun Wei, Ananya Singh, Mark H Lemley, Serge D Van Kriekinge, Paul B Kavanagh, Heidi Gransar, Donghee Han, Sean W Hayes and 5 more

Open access · hybridAbstract read
In one paragraph

Article in European journal of nuclear medicine and molecular imaging, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
1.6field-weighted citation impact, top 18% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. Automatic motion correction for myocardial blood flow estimation improves diagnostic performance for coronary artery disease inJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025
    Trial
  2. Review
  3. Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

15 authors at 2 institutions in 3 countries.

Keiichiro KuronumaDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Robert J H MillerDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Chih-Chun WeiDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Ananya SinghDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Mark H LemleyDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Serge D Van KriekingeDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Paul B KavanaghDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Heidi GransarDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Donghee HanDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Sean W HayesDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Louise ThomsonDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Damini DeyDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
John D FriedmanDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Daniel S BermanDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA.
Piotr J SlomkaDepartments of Medicine (Division of Artificial Intelligence in Medicine), Imaging, and Biomedical Sciences, Cedars-Sinai Medical Center, 8700 Beverly Blvd, Los Angeles, CA, 90048, USA. Piotr.Slomka@cshs.org.ORCID 0000-0002-6110-938X
Cedars-Sinai Medical Center · USNihon University · JP

Funding

Integrating Artificial Intelligence for Optimal Analysis of CardiacPET/CTR01EB034586 · NIBIB · CEDARS-SINAI MEDICAL CENTER · PI DI CARLI, MARCELO F, SLOMKA, PIOTR J · 2022 to 2025
$2.9M
NIBIB NIH HHS R01 EB034586
6 · The paper itself

Abstract

purposeThe myocardial creep is a phenomenon in which the heart moves from its original position during stress-dynamic PET myocardial perfusion imaging (MPI) that can confound myocardial blood flow measurements. Therefore, myocardial motion correction is important to obtain reliable myocardial flow quantification. However, the clinical importance of the magnitude of myocardial creep has not been explored. We aimed to explore the prognostic value of myocardial creep quantified by an automated motion correction algorithm beyond traditional PET-MPI imaging variables.

methodsConsecutive patients undergoing regadenoson rest-stress [

resultsA total of 4,276 patients (median age 71 years; 60% male) were analyzed, and 1,007 ACM events were documented during a 5-year median follow-up. Processing time for automatic motion correction was < 12 s per patient. Myocardial creep in the superior to inferior (downward) direction was greater than the other directions (median, 4.2 mm vs. 1.3-1.7 mm). Annual mortality rates adjusted for age and sex were reduced with a larger downward creep, with a 4.2-fold ratio between the first (0 mm motion) and 10th decile (11 mm motion) (mortality, 7.9% vs. 1.9%/year). Downward creep was associated with lower ACM after full adjustment for clinical and imaging parameters (adjusted hazard ratio, 0.93; 95%CI, 0.91-0.95; p < 0.001). Adding downward creep to the standard PET-MPI imaging model significantly improved ACM prediction (area under the receiver operating characteristics curve, 0.790 vs. 0.775; p < 0.001), but other directions did not (p > 0.5).

conclusionsDownward myocardial creep during regadenoson stress carries additional information for the prediction of ACM beyond conventional flow and perfusion PET-MPI. This novel imaging biomarker is quantified automatically and rapidly from stress dynamic PET-MPI.

Indexed as

HeartMyocardial Perfusion ImagingPositron-Emission TomographyAgedFemaleHumansMaleMiddle AgedMyocardiumPrognosisRubidium RadioisotopesStress, PhysiologicalRubidium RadioisotopesDynamic myocardial perfusion imagingMyocardial flow reservePositron emission tomographyRegadenosonRubidium

Identifiers

PMID38253908
PMCPMC11042981
OpenAlexW4391116613

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.